Ploty

library(tidyverse)
library(viridis)
library(plotly)
library(p8105.datasets)
data(ny_noaa)

nynoaa <- 
  ny_noaa %>% 
  janitor::clean_names() %>%
  separate(date, into = c("Year", "Month", "Day"), sep = "-") %>% 
  janitor::clean_names() 

ploty 1

ny_df_1 =
  nynoaa %>% 
  group_by(month,id) %>% 
  mutate(tmax = as.numeric(tmax)) %>% 
  mutate(tmin = as.numeric(tmin)) %>% 
  na.omit() %>% 
  summarise(mean_temp = mean(tmax)) %>% 
  ungroup

ny_df_1 %>% 
plot_ly(x = ~month, y = ~mean_temp, type = "scatter", mode = "markers",
          alpha = 0.5, 
          color = ~mean_temp,
          text = ~id)

ploty 2

ny_df_2 =
  nynoaa %>% 
  group_by(year,id) %>% 
  mutate(tmax = as.numeric(tmax)) %>% 
  mutate(tmin = as.numeric(tmin)) %>% 
  na.omit() %>% 
  summarise(mean_temp = mean(tmax)) %>% 
  ungroup

ny_df_2 %>% 
   plot_ly(x = ~year, y = ~mean_temp, color = ~year, type = "box")

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

plot 3

ny_df_3 <-nynoaa %>% 
  group_by(year, month) %>% 
  na.omit(tmax) %>% 
  na.omit(tmin) %>% 
  mutate(tmax_2 = as.numeric(tmax, na.rm = TRUE)) %>% 
  mutate(tmin_2 = as.numeric(tmin, na.rm = TRUE)) %>%
  ungroup %>% 
  ggplot(aes(tmin_2,tmax_2))+
  geom_hex(bins = 15)+
  theme(legend.position = "bottom")

ggplotly(ny_df_3)